# Depth Anything V2 — Monocular Depth Estimation at Scale

> Depth Anything V2 is a family of monocular depth estimation models that predict accurate relative depth maps from single RGB images, trained on a massive dataset of 62 million labeled images.

## Install

Save as a script file and run:

# Depth Anything V2 — Monocular Depth Estimation at Scale

## Quick Use
```bash
pip install torch torchvision huggingface_hub
python -c "
from depth_anything_v2.dpt import DepthAnythingV2
import torch
model = DepthAnythingV2(encoder='vitb', features=128, out_channels=[96, 192, 384, 768])
model.load_state_dict(torch.load('depth_anything_v2_vitb.pth', map_location='cpu'))
model.eval()
# Predict depth from an image
import cv2
img = cv2.imread('photo.jpg')
depth = model.infer_image(img)
print(depth.shape)
"
```

## Introduction
Depth Anything V2 is a monocular depth estimation foundation model developed by researchers at the University of Hong Kong and TikTok. It predicts per-pixel relative depth from a single RGB image with high detail and edge preservation, trained through a pipeline that leverages both large-scale unlabeled data and precise synthetic depth labels.

## What Depth Anything V2 Does
- Predicts dense relative depth maps from single RGB images
- Provides models in three sizes: ViT-S (24.8M), ViT-B (97.5M), and ViT-L (335.3M)
- Handles diverse scenes including indoor, outdoor, close-up, and wide-angle views
- Generates metric depth estimates when fine-tuned with metric depth labels
- Supports video depth estimation with temporal consistency processing

## Architecture Overview
Depth Anything V2 uses a DPT (Dense Prediction Transformer) architecture with a DINOv2 backbone as the encoder. The key training innovation is a two-stage pipeline: first, a teacher model is trained on precise synthetic depth data from virtual environments; then, the teacher generates pseudo-labels for 62 million real-world unlabeled images. The student model learns from this combined synthetic and pseudo-labeled dataset, achieving both the precision of synthetic labels and the diversity of real-world data.

## Self-Hosting & Configuration
- Clone the repository and download pre-trained model weights from Hugging Face
- ViT-S model runs on GPUs with as little as 2 GB VRAM for single-image inference
- ViT-L model requires 4 GB+ VRAM and delivers the highest accuracy
- Inference runs at 30+ FPS with the small model on consumer GPUs
- Metric depth fine-tuned models available for indoor and outdoor use cases

## Key Features
- Precise edge-aware depth predictions with sharp boundaries between objects
- Synthetic-to-real training pipeline scales without manual depth annotations
- Three model sizes for flexible accuracy-speed tradeoffs
- Fine-grained detail preservation for small and thin structures
- Compatible with downstream 3D tasks like novel view synthesis and point cloud generation

## Comparison with Similar Tools
- **MiDaS** — Intel monocular depth model, pioneered the field but lower accuracy than V2
- **ZoeDepth** — combines relative and metric depth estimation, less scalable training
- **Marigold** — diffusion-based depth with high detail but significantly slower inference
- **Metric3D** — metric depth estimation focused on scale-aware predictions
- **UniDepth** — universal depth model aiming for metric depth across camera types

## FAQ
**Q: What is the difference between relative and metric depth?**
A: Relative depth predicts which pixels are closer or farther without absolute scale. Metric depth estimates actual distances in meters and requires calibration or fine-tuning.

**Q: Can Depth Anything V2 process video?**
A: Yes, process frames individually for basic use. The project also provides a video-specific pipeline with temporal smoothing for consistent depth across frames.

**Q: How does V2 improve over V1?**
A: V2 replaces the DINOv2-based self-training with a synthetic-to-real pipeline, yielding sharper depth edges, fewer artifacts, and improved accuracy on benchmarks.

**Q: Does it work on images with transparent or reflective surfaces?**
A: Performance degrades on highly reflective, transparent, or featureless surfaces, as these are inherently ambiguous for monocular depth estimation.

## Sources
- https://github.com/DepthAnything/Depth-Anything-V2
- https://depth-anything-v2.github.io/

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Source: https://tokrepo.com/en/workflows/asset-0219e18b
Author: Script Depot